European journal of radiology · 2019
Assessment of tumor heterogeneity: Differentiation of periampullary neoplasms based on CT whole-lesion histogram analysis.
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Lu Jingyu, Hu Daoyu, Tang Hao, Hu Xuemei, Shen Yaqi, Li Zhen, Peng Yang, Kamel Ihab
Аннотация
PURPOSE: To investigate the utility of whole-lesion histogram analysis from multidetector computed tomography (MDCT) for discrimination of duodenal adenocarcinoma (DAC), pancreatic ductal adenocarcinoma (PDAC) and gastrointestinal stromal tumor (GIST) around the periampullary area.
MATERIALS AND METHODS: 171 patients suspicious of periampullary tumors were examined by MDCT (arterial and venous phases) and treated with surgery. A total of 74 patients were finally included in this retrospective study (26 DACs, 20 PDACs, and 28 GISTs). The interobserver agreement was evaluated by intra-class correlation coefficient (ICC) test between two radiologists. Volumetric histogram analysis based on CT Kinetics software was performed on enhanced MDCT images that recorded different histogram parameters of arterial and venous phases, including mean, median, 10th, 25th, 75th, and 90th percentiles, as well as skewness, kurtosis and entropy. The extracted histogram parameters were compared between DAC, PDAC and GIST respectively by Mann-Whitney U tests with Bonferroni corrections. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic ability of each significant parameter and the area under the curve (AUC) was calculated.
RESULTS: The whole-lesion CT histogram analysis demonstrated significant differences between DAC, PDAC, and GIST with different histogram features on both arterial and venous phase scans (all P < 0.05). In the ROC analysis, the 90th percentile of venous phase demonstrated the highest AUC of 0.854 (P < 0.001) for discriminating DAC from PDAC. Excellent discriminators of periampullary tumors were noted among the histogram features, namely the 90th percentile of arterial phase, which demonstrated AUCs of 0.809 and 0.936 (P < 0.001) respectively for distinguishing DAC and PDAC from GIST.
CONCLUSION: The whole-lesion CT histogram analysis could be useful for differential diagnosis of DAC, PDAC and GIST arising from the periampullary area. Further assessment is warranted to investigate the clinical role of histogram analysis based on MDCT.
MATERIALS AND METHODS: 171 patients suspicious of periampullary tumors were examined by MDCT (arterial and venous phases) and treated with surgery. A total of 74 patients were finally included in this retrospective study (26 DACs, 20 PDACs, and 28 GISTs). The interobserver agreement was evaluated by intra-class correlation coefficient (ICC) test between two radiologists. Volumetric histogram analysis based on CT Kinetics software was performed on enhanced MDCT images that recorded different histogram parameters of arterial and venous phases, including mean, median, 10th, 25th, 75th, and 90th percentiles, as well as skewness, kurtosis and entropy. The extracted histogram parameters were compared between DAC, PDAC and GIST respectively by Mann-Whitney U tests with Bonferroni corrections. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic ability of each significant parameter and the area under the curve (AUC) was calculated.
RESULTS: The whole-lesion CT histogram analysis demonstrated significant differences between DAC, PDAC, and GIST with different histogram features on both arterial and venous phase scans (all P < 0.05). In the ROC analysis, the 90th percentile of venous phase demonstrated the highest AUC of 0.854 (P < 0.001) for discriminating DAC from PDAC. Excellent discriminators of periampullary tumors were noted among the histogram features, namely the 90th percentile of arterial phase, which demonstrated AUCs of 0.809 and 0.936 (P < 0.001) respectively for distinguishing DAC and PDAC from GIST.
CONCLUSION: The whole-lesion CT histogram analysis could be useful for differential diagnosis of DAC, PDAC and GIST arising from the periampullary area. Further assessment is warranted to investigate the clinical role of histogram analysis based on MDCT.